The use of artificial intelligence will transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a digital revolution is transforming the reporting practice of diagnostic histopathology and this has sparked a proliferation of image analysis software tools. While this is an exciting development that could discover novel predictive clinical information and potentially address international pathology workforce shortages, there is a clear need for a robust and evidence‐based framework in which to develop these new tools in a collaborative manner that meets regulatory approval. With these issues in mind, the NCRI Cellular Molecular Pathology (CM‐Path) initiative and the British In Vitro Diagnostics Association (BIVDA) have set out a roadmap to help academia, industry, and clinicians develop new software tools to the point of approved clinical use. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.

The measures to control the COVID-19 outbreak will likely remain a feature of our working lives until a suitable vaccine or treatment is found. The pandemic has had a substantial impact on clinical services, including cancer pathways. Pathologists are working remotely in many circumstances to protect themselves, colleagues, family members and the delivery of clinical services. The effects of COVID-19 on research and clinical trials have also been significant with changes to protocols, suspensions of studies and redeployment of resources to COVID-19. In this article, we explore the specific impact of COVID-19 on clinical and academic pathology and explore how digital pathology and artificial intelligence can play a key role to safeguarding clinical services and pathology-based research in the current climate and in the future.

Ectopic crypt foci and cytoplasmic eosinophilia are encountered in sporadic VA/TVAs but not to the same extent and degree as in TSA. ECFs were found in one-third of cases, but cytoplasmic eosinophilia is rare. The pattern of luminal serration in TSA is very characteristic and not recapitulated in VA/TVA. The occurrence of all three histological features together occurs only in TSA. ECFs are not a sine qua non for TSA and are encountered commonly in VA/TVAs. VA/TVAs often contain occasional glands typical of TSA.

More than 50% of TSAs are associated with a precursor lesion or adjacent TA/TVA. Their recognition is important as this may have surveillance and management ramifications.

AimsTo evaluate if a deep learning algorithm can be trained to identify tumour-infiltrating lymphocytes (TILs) in tissue samples of testicular germ cell tumours and to assess whether the TIL counts correlate with relapse status of the patient.MethodsTILs were manually annotated in 259 tumour regions from 28 whole-slide images (WSIs) of H&E-stained tissue samples. A deep learning algorithm was trained on half of the regions and tested on the other half. The algorithm was further applied to larger areas of tumour WSIs from 89 patients and correlated with clinicopathological data.ResultsA correlation coefficient of 0.89 was achieved when comparing the algorithm with the manual TIL count in the test set of images in which TILs were present (n=47). In the WSI regions from the 89 patient samples, the median TIL density was 1009/mm2. In seminomas, none of the relapsed patients belonged to the highest TIL density tertile (>2011/mm2). TIL quantifications performed visually by three pathologists on the same tumours were not significantly associated with outcome. The average interobserver agreement between the pathologists when assigning a patient into TIL tertiles was 0.32 (Kappa test) compared with 0.35 between the algorithm and the experts, respectively. A higher TIL density was associated with a lower clinical tumour stage, seminoma histology and lack of lymphovascular invasion.ConclusionsDeep learning–based image analysis can be used for detecting TILs in testicular germ cell cancer more objectively and it has potential for use as a prognostic marker for disease relapse.

The COVID-19 pandemic has challenged our diagnostic services at a time when many histopathology departments already faced a diminishing workforce and increasing workload. Digital pathology (DP) has been hailed as a potential solution to at least some of the challenges faced. We present a survey of pathologists within a UK National Health Service cellular pathology department with access to DP, in which we ascertain the role of DP in clinical services during this current pandemic and explore challenges encountered. This survey indicates an increase in uptake of diagnostic DP during this period, with increased remote access. Half of respondents agreed that DP had facilitated maintenance of diagnostic practice. While challenges have been encountered, these are remediable, and none have impacted on the uptake of DP during this period. We conclude that in our institution, DP has demonstrated current and future potential to increase resilience in diagnostic practice and have highlighted some of the challenges that need to be considered.

Since the advent of monoclonal antibodies against epidermal growth factor receptor (EGFR) in colorectal cancer therapy, the determination of RAS mutational status is needed for therapeutic decision-making. Most prevalent in colorectal cancer are KRAS exon 2 mutations (40% prevalence); lower prevalence is observed for KRAS exon 3 and 4 mutations (6%) and NRAS exon 2, 3, and 4 mutations (5%). The Idylla™ KRAS Mutation Test on the molecular diagnostics Idylla™ platform is a simple (<2 minutes hands-on time), highly reliable, and rapid (approximately 2 hours turnaround time) in vitro diagnostic sample-to-result solution. This test enables qualitative detection of 21 mutations in codons 12, 13, 59, 61, 117, and 146 of the KRAS oncogene being clinically relevant according to the latest clinical guidelines. Here, the performance of the Idylla™ KRAS Mutation Assay, for Research Use Only, was assessed on archived formalin-fixed paraffin-embedded (FFPE) tissue sections by comparing its results with the results previously obtained by routine reference approaches for KRAS genotyping. In case of discordance, samples were assessed further by additional methods. Among the 374 colorectal cancer FFPE samples tested, the overall concordance between the Idylla™ KRAS Mutation Assay and the confirmed reference routine test results was found to be 98.9%. The Idylla™ KRAS Mutation Assay enabled detection of 5 additional KRAS-mutated samples not detected previously with reference methods. As conclusion the Idylla™ KRAS Mutation Test can be applied as routine tool in any clinical setting, without needing molecular infrastructure or expertise, to guide the personalized treatment of colorectal cancer patients.